The field of the invention is manufacturing semiconductor and similar micro-scale devices. More specifically, the invention related to scatterometry, which is a technique for measuring micro-scale features, based on the detection and analysis of light scattered from the surface. Generally, scatterometry involves collecting the intensity of light scattered or diffracted by a periodic feature, such as a grating structure as a function of incident light wavelength or angle. The collected signal is called a signature, since its detailed behavior is uniquely related to the physical and optical parameters of the structure grating.
Scatterometry is commonly used in photolithographic manufacture of semiconductor devices, especially in overlay measurement, which is a measure of the alignment of the layers which are used to form the devices. Accurate measurement and control of alignment of such layers is important in maintaining a high level of manufacturing efficiency.
Scatterometry measurements are made by finding the closest fit between an experimentally obtained signature and one obtained by other means and for which the value of the property or properties to be measured are known. Commonly, the second known signature, also known as the reference signature, is calculated from a rigorous model of the scattering process. It may occasionally be determined experimentally. Where a modeled signature is used as the reference, either the calculations are performed once and all signatures possible for the parameters of the grating that may vary are stored in a library, or the signature is calculated when needed for test values of the measured parameters.
However the reference signature is obtained, a comparison of the experimental and reference signature is made. The comparison is quantified by a value which indicates how closely the two signatures match. Commonly, the fit quality is calculated as the root-mean-square difference (or error) (RMSE) between the two signatures, but other comparison methods may be used. The measurement is made by finding the reference signal with the best value of fit quality to the experimental signature. The measurement result is then the parameter set used to calculate the reference signal. In the case of experimentally derived reference signatures, the reference signal is the value of the known parameters used to generate the experimental signature. As with any real system, the experimental signature obtained from the metrology system will contain some noise. This creates a lower limit to the fit quality that can be expected.
Microelectronic devices and feature sizes continue to get ever smaller. The requirement for the precision of overlay measurement of 130 nm node is 3.5 nm, and that of 90 nm node is 3.2 nm. For the next-generation semiconductor manufacturing process of 65 nm node, the requirement for the precision of overlay measurement is 2.3 nm. Since scatterometry has good repeatability and reproducibility, it would be advantageous to be able to use it in the next generation process. However, conventional bright-field metrology systems are limited by the image resolution. Consequently, these factors create significant technological challenges to the use of scatterometry with increasingly smaller features.
Conventional methods compare diffraction spectrums of unknown measurement with simulated diffraction spectrums. Methods such as the Levenberg Marquardt optimization, random search and genetic algorithm, compare the measured diffraction spectrum with an on-line generated simulated diffraction spectrum. This method is slow but can be used to measure a fully unknown grating. Other conventional methods such as principal component regression (PCR), partial least square (PLS), inverse least square (ILS) and artificial neural network (ANN), build a diffraction library in advance, and the measured diffraction spectrum is compared with the diffraction spectrums in the library to find a closest fit spectrum. This method can increase the processing speed, but needs more computer storage capacity than the first method. Methods such as described in U.S. Pat. No. 6,785,638 and U.S. Pat. No. 6,768,967, integrate both of these methods to increase the processing speed and decrease the storage capacity, but the algorithm used is much more complicated.
Conventional methods use static equations such as root mean square error (RMSE), mean square error (MSE) and square distance (SD) to compare the measured diffraction spectrum with the simulated diffraction spectrum entirely. However, RMSE or MSE average the entire diffraction spectrum, which leads to a region with a smaller variation, decreasing the entire comparison performance. Further, SD does not average the variation of variable as RMSE or MSE does, but it is much more sensitive to noise.